@misc{TrappDoellner2019, author = {Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Interactive Close-Up Rendering for Detail plus Overview Visualization of 3D Digital Terrain Models}, series = {2019 23rd International Conference Information Visualisation (IV)}, journal = {2019 23rd International Conference Information Visualisation (IV)}, editor = {Banissi, E Ursyn}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Los Alamitos}, isbn = {978-1-7281-2838-2}, issn = {2375-0138}, doi = {10.1109/IV.2019.00053}, pages = {275 -- 280}, year = {2019}, abstract = {This paper presents an interactive rendering technique for detail+overview visualization of 3D digital terrain models using interactive close-ups. A close-up is an alternative presentation of input data varying with respect to geometrical scale, mapping, appearance, as well as Level-of-Detail (LOD) and Level-of-Abstraction (LOA) used. The presented 3D close-up approach enables in-situ comparison of multiple Regionof-Interests (ROIs) simultaneously. We describe a GPU-based rendering technique for the image-synthesis of multiple close-ups in real-time.}, language = {en} } @article{ReimannKlingbeilPasewaldtetal.2019, author = {Reimann, Max and Klingbeil, Mandy and Pasewaldt, Sebastian and Semmo, Amir and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Locally controllable neural style transfer on mobile devices}, series = {The Visual Computer}, volume = {35}, journal = {The Visual Computer}, number = {11}, publisher = {Springer}, address = {New York}, issn = {0178-2789}, doi = {10.1007/s00371-019-01654-1}, pages = {1531 -- 1547}, year = {2019}, abstract = {Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. In this work, we first propose a problem characterization of interactive style transfer representing a trade-off between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, we enhance state-of-the-art neural style transfer techniques by mask-based loss terms that can be interactively parameterized by a generalized user interface to facilitate a creative and localized editing process. We report on a usability study and an online survey that demonstrate the ability of our app to transfer styles at improved semantic plausibility.}, language = {en} } @misc{TrappDoellner2019, author = {Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Real-time Screen-space Geometry Draping for 3D Digital Terrain Models}, series = {2019 23rd International Conference Information Visualisation (IV)}, journal = {2019 23rd International Conference Information Visualisation (IV)}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Los Alamitos}, isbn = {978-1-7281-2838-2}, issn = {2375-0138}, doi = {10.1109/IV.2019.00054}, pages = {281 -- 286}, year = {2019}, abstract = {A fundamental task in 3D geovisualization and GIS applications is the visualization of vector data that can represent features such as transportation networks or land use coverage. Mapping or draping vector data represented by geometric primitives (e.g., polylines or polygons) to 3D digital elevation or 3D digital terrain models is a challenging task. We present an interactive GPU-based approach that performs geometry-based draping of vector data on per-frame basis using an image-based representation of a 3D digital elevation or terrain model only.}, language = {en} } @misc{FlorioTrappDoellner2019, author = {Florio, Alessandro and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Semantic-driven Visualization Techniques for Interactive Exploration of 3D Indoor Models}, series = {2019 23rd International Conference Information Visualisation (IV)}, journal = {2019 23rd International Conference Information Visualisation (IV)}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Los Alamitos}, isbn = {978-1-7281-2838-2}, issn = {2375-0138}, doi = {10.1109/IV.2019.00014}, pages = {25 -- 30}, year = {2019}, abstract = {The availability of detailed virtual 3D building models including representations of indoor elements, allows for a wide number of applications requiring effective exploration and navigation functionality. Depending on the application context, users should be enabled to focus on specific Objects-of-Interests (OOIs) or important building elements. This requires approaches to filtering building parts as well as techniques to visualize important building objects and their relations. For it, this paper explores the application and combination of interactive rendering techniques as well as their semanticallydriven configuration in the context of 3D indoor models.}, language = {en} } @article{StojanovicTrappRichteretal.2019, author = {Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Service-oriented semantic enrichment of indoor point clouds using octree-based multiview classification}, series = {Graphical Models}, volume = {105}, journal = {Graphical Models}, publisher = {Elsevier}, address = {San Diego}, issn = {1524-0703}, doi = {10.1016/j.gmod.2019.101039}, pages = {18}, year = {2019}, abstract = {The use of Building Information Modeling (BIM) for Facility Management (FM) in the Operation and Maintenance (O\&M) stages of the building life-cycle is intended to bridge the gap between operations and digital data, but lacks the functionality of assessing the state of the built environment due to non-automated generation of associated semantics. 3D point clouds can be used to capture the physical state of the built environment, but also lack these associated semantics. A prototypical implementation of a service-oriented architecture for classification of indoor point cloud scenes of office environments is presented, using multiview classification. The multiview classification approach is tested using a retrained Convolutional Neural Network (CNN) model - Inception V3. The presented approach for classifying common office furniture objects (chairs, sofas and desks), contained in 3D point cloud scans, is tested and evaluated. The results show that the presented approach can classify common office furniture up to an acceptable degree of accuracy, and is suitable for quick and robust semantics approximation - based on RGB (red, green and blue color channel) cubemap images of the octree partitioned areas of the 3D point cloud scan. Additional methods for web-based 3D visualization, editing and annotation of point clouds are also discussed. Using the described approach, captured scans of indoor environments can be semantically enriched using object annotations derived from multiview classification results. Furthermore, the presented approach is suited for semantic enrichment of lower resolution indoor point clouds acquired using commodity mobile devices.}, language = {en} }